Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches

Fengxiao Tang, Yuichi Kawamoto, Nei Kato, Jiajia Liu

Research output: Contribution to journalArticlepeer-review

272 Citations (Scopus)


As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.

Original languageEnglish
Article number8926369
Pages (from-to)292-307
Number of pages16
JournalProceedings of the IEEE
Issue number2
Publication statusPublished - 2020 Feb


  • 6G
  • Internet of Vehicles (IoV)
  • deep learning
  • intelligent radio (IR)
  • intelligentization
  • machine learning (ML)
  • resource allocation
  • routing
  • security
  • space-air-ground
  • traffic control
  • vehicle-to-everything (V2X)
  • vehicle-to-vehicle (V2V)
  • vehicular network

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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